In this thesis, we analyzed neural activity data obtained from transgenic mice (expression GCaMP3, genetically encoded calcium indicator) during tasks/reward sessions. Using Ising’s model for analysis of spin chain dynamics, we want to find the probability of each state and understand the structure of the network. The first step is to solve the inverse problem and find the parameters of the Ising model. In addition to the exact optimization of the Ising model parameters, we apply several approximate methods that we compare with exact optimization. In the second part, we describe how machine learning methods are a natural extension of inverse problems in physics. Using machine learning, we are able to not only answer various questions, but we can define a metric of precision. To the question in which state will neuron be in the next moment, we are able to answer with the precision of 92 %. We also describe an algorithm that predicts the state of the entire neuronal network, not just one neuron. For a network that consists of 37 neurons, we can predict the network state in the next moment with an average error of less than a quarter of the neurons per instance. Using the machine learning methods, we classify each individual measured neuronal state into the state of solving the task, or the state of reward. Not only that we can classify states with accuracy greater than 95%, but we can also understand the activity of which neurons signal that mouse is solving the task or receiving the reward.